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For a long time, the conventional wisdom in enterprise software implementations seemed to follow the old adage:

“There is nothing that can be done in a short time that can’t be done just as well in a long time.”

However, another old adage is “time is money” and there are many time-sensitive costs associated with enterprise software implementations that greatly increase their total cost of ownership and greatly decrease their total return on investment, including:

• Software Installation and Configuration
• Application Development
• Production Deployment
• Application Maintenance
• Software Enhancements and Bug Fixes
• Software Upgrades

We have also become conditioned to expect that a typical enterprise information initiative requires between 6 to 18 months to deploy, even when employing a agile (or phased) approach to application development.  However, with Gartner estimating that the volume of enterprise data also doubles every 18 months, the very challenge for which the enterprise is implementing a solution becomes a moving target.  The ground is therefore constantly shifting beneath our feet.

Day-to-day business challenges are evolving faster than the applications under development can come on-line – and by the time they do, the underlying business requirements they were based on have changed, forcing end-users to attempt to manually close the gap.  Basically, the business is being forced to adapt to the technology.

The current economic recession is also forcing everyone to try to find ways of “doing more with less.”  Some have been looking into the potential cost savings and operational efficiencies offered by cloud computing and software-as-a-service (SaaS).  But perhaps we should also be challenging vendors to not only provide enterprise software that is easier to install, configure, develop, deploy, and maintain – but that is also adaptive to changing business logic.

In order to remain competitive in such a constantly evolving environment, we need technology that adapts to business needs.  We need adaptive software.

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In the 1970s, spell checking programs debuted on mainframe computers.  In the 1980s, personal computers arrived and spell checkers soon followed, especially embedded within word processors.  In the 1990s, spell checkers joined the Internet revolution and debuted in e-mail programs and web browsers.

Today, spell checkers are virtually everywhere, from mobile text messaging to social networking websites.  In fact, nowadays you would be surprised (and probably annoyed) if spell checking wasn’t available whenever and wherever you were entering text.

Spell checkers are far from perfect.  However, their greatest feature is not just telling you that you have typed a word it can’t find an exact match for in its dictionary – it also provides a list of potential matches that may contain the word you were trying to spell.

In my previous post Apples and Oranges, I asked how many times you have looked up a contact in your e-mail program only to get zero results back.  Now, this might be understandable if you were looking up the e-mail address for Apple by typing in Orange.  However, most of the time what you typed was off by just a single letter (i.e. Aple).

Practically all of the devices and applications we use on a daily basis provide only exact matching.  Isn’t this just as surprising and annoying as not providing a spell checker?

For goodness sake, it’s 2009. Why does this problem persist – especially since the core technology to virtually eradicate this problem exists. Shouldn’t we demand the standard functionality of a Data Checker?

A Data Checker wouldn’t be clairvoyant.  However, if it couldn’t find an exact match for your lookup, it would provide a ranked list of potential matches.

Shouldn’t all of our devices and applications provide the capability of overcoming data variations, lack of standards, omissions, and other inconsistencies in our lookups and searches?

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All industries are feeling the effects of the current economic recession.  All industries are interested in the many benefits of innovation.  However, in the technology industry, not only is it a significant interest, innovation is the driving force and lifeblood of a rapidly evolving sector.

Historically, many of the most powerful new trends in technology originated from small entrepreneurial ventures.  Small technology vendors tend to be specialists with a narrow focus that can provide a great source of innovation.  Small vendors are usually less financially stable and rely mainly on funding for their early survival.  However, the current economy is limiting venture capital opportunities.

Large technology vendors tend to innovate via acquisition of these smaller vendors.  A continuing trend in information technology is the consolidation of acquired functionality into enterprise class application development platforms with integrated components for data quality, data integration, master data management and business intelligence.  This allows large vendors to offer end-to-end solutions and the convenience of one-vendor information technology shopping.

However, further innovation is typically delayed while the vendor prioritizes integrating the acquired technology into the existing suite of products and integrating the acquired people into their existing staff.  Additionally, training programs and sales strategies must be adjusted to reflect the updated platform and product offerings.  All of this requires significant time and effort.  The collateral damage is innovation can lose momentum or become stagnant.

Even before the economic recession, many in the information technology industry expressed concern about the effect of this vendor consolidation on innovation.  Now with the economy starting to claim some promising start-ups, are we looking at an innovation recession?

Of course, the situation is more complicated than small vendors vs. large vendors.  Vendors of all sizes are struggling with finding viable business models to pursue possibilities such as cloud computing and software-as-a-service (SaaS).

The trend with most innovations is that early adopters often spend more than they earn in these pursuits.  Innovation is often high-risk with no guarantee of high-reward. Entrepreneurial start-ups are usually more willing to go “all-in” and risk everything, but again venture capital is currently hard to come by.  Large vendors have more financial stability, but in a down economy it is often better even for them to play it safe.

Everyone wants to do more with less.  But some are settling to simply do less – long term (or even medium term) this is a losing strategy.  Innovation often stimulates the economy but the paradox is that an economic recession both spurs and suppresses innovation.

With many information business technology professionals feeling forced to find a way to survive, has do more with less become do without innovation or is it truly do with innovation?

My belief is that it’s the latter.

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The Cloud brings Commoditization

A lot has been written (and rightfully so) about the need to better align and to foster collaboration between the Business and Information Technology (IT). Traditionally this has been necessary because of the typically disparate skills and backgrounds of the people working on the business and technical sides of the enterprise.

In the early days of information technology when computers represented a brave new world that had such people in it that were typically electrical engineers and mathematicians, they spoke a language that the business world didn’t understand. They were so consumed with the difficulties and intricacies of the emerging technology that they had no time to dedicate to day to day business matters.

Today, rapid advancements in technology are becoming so commonplace that the next generation of college graduates will never have lived in a world without the Internet, laptop computers and cell phones that have more processing power and functionality than even the best desktop computers had less than two decades ago.

Our brave new world is seeing a dramatic increase in number of people with skills and knowledge in both business and technology and I believe that in the near future Information Tecnology (IT) will transition simply to Business Technology (BT).

If you ask me, it’s long overdue.

For individuals, families, businesses and organizations the theme for 2009 will undoubtedly be “Do More with Less.” The Google count is a mere 794,000 right now – I’m curious to see where it ends up 12 months from now.

For business and organizations doing more with less in these fraught times is imperative for survival. On the IT side, technology professionals at all levels must stay focused on delivering business value more efficiently and effectively than ever before.

One of the key enablers for doing more with less is innovation. Innovation delivers advantage – in the the form of saving costs, saving effort and improving results.

As we are all asked to do more with less let’s not forget that driving innovation is one way to make it happen.

Netrics is all about delivering innovative enterprise software! I’ll write about some specific examples in upcoming posts.

“The Cloud” buzz continues to grow and will certainly be one of the most talked about topics in 2009. In fact, it’s probably already on the top 10 list for 2008 – a quick Google search shows nine million matches for “cloud computing.”

I think it’s interesting to look at first wave of changes that The Cloud will bring. As more and more relatively low-value but important IT services are delivered by SaaS providers, internal IT resources will be better able to focus on higher value projects: dealing with business transformation, working on effectiveness and efficiency projects and, of course, trying once more to deal effectively with increasingly high mountains of imperfect data.

What are some examples that are already happening? CRM and corporate email immediately jump to mind. Both are critical to organizations but at the same time both are being commoditized by The Cloud.

Case in point, Netrics – both our CRM (Salesforce.com) and our corporate email (Microsoft Exchange Server) are handled for us by outside vendors. At a fraction of what it would cost us to do it – and if allows us to better allocate our technical resources – keeping them focused on enhancing and improving our products!

The average computer user is already well aware that computers are limited in how they “look” at data. Ask yourself how many times you looked up a contact in Outlook (or whatever app you use to store your contacts) only to get zero results back… why? Because what you typed was off by just a single letter.

It seems pretty silly to us humans but as far as computers are concerned the result of comparing

“Damianakis” and “Damanakis”

is exactly the same result of comparing

“Damianakis” and “Smith”

or

“apples” and “oranges”

or

“Rahiem A Griffin” and “Rahiem Griffin”

when computers do these comparisons, the result is always “zero” – they are not equal. Which is clearly wrong for the first and last examples.

This problem has been around since the invention of the computer – comparing data elements (bytes) is built into every modern CPU. The comparison is, of course, limited to testing for equality or inequality.

This works well in a world where data is always perfect. But that’s not the real world – in the real world, data is never perfect – there are always small differences, inconsistencies, and errors that humans can easily handle but computers can’t.

So, why does this inability to compare data matter?

Because it is the root cause of many problems – problems that are created when we rely on computers to help us. A few examples,

• airlines want to be sure they don’t let passengers on the no-fly list board planes
• banks want to be sure they don’t transact business with people and organizations they’re not allowed to
• hospitals want to be find the right patient record
• companies want to know who their customers are
• state agencies want to know when citizens are repeatedly exposed to health risks (ie, pesticides, lead, etc)
• law enforcement agencies want to scan their data to find particular criminals
• and the list goes on and on

You can build a database of patients or drivers or criminals or products, etc… but after the data is collected how to do you use it effectively? If you rely on exact matching you will run in problems. Unfortunately, exact matching is all that database systems offer.

The bottom line is that this core limitation translates in a broad range of problems that affect all of us.

Where does “Rahiem Griffin” come from? A while back David Rubinstein covered a tragic story that might have been avoided if computers were better able to handle real-world (ie, imperfect) data.

Our thoughts and prayers go out to Officer Kenneth Baribault and his family.

I came across this recent article in BtoB Magazine

BtoB Magazine: “Marketers: Clean customer data a priority in 2008”
By Carol Krol
March 17, 2008

It’s great to see that simply collecting customer data is no longer good enough – the data needs to be usable to benefit the business.

Refining customer data quality and access to customer data have emerged as two of the top marketing investment priorities of b-to-b CMOs this year. Half of b-to-b marketers plan to put more resources against creating marketing databases, cleaning up customer data, improving sales force automation and CRM integration, according to Forrester Research in its “B2B CMO Investment Priorities for 2008” report.

The article references a recent Forrester Research report, a survey released by Alterian, and quotes several marketing executives at large well-known enterprises – all agree that something must be done. Dealing with imperfect customer data is now a top priority for 2008. Finally!

Now that we all agree on the problem, the question is how should this problem be solved? Of course, it’s often a multi-facetted, cross-organizational solution that will always involve Technology, Process, and People (TPP). But all three components of TPP depend on each other – and any one of the three can be the weak link in the chain.

But in most cases, the weakest link is Technology – which is why more People are thrown at a problem, which in turn requires more Process to keep everyone working together efficiently. Then you get the snowball effect and the solution grows out of control and takes on a life (and expense) of its own.

So, the argument is if we can improve the core Technology, then we can reduce the People part and streamline the Process part as well. The TPP solution will stay nicely under control and perform as it’s expected to perform – while keeping costs in check.

Which leads to this wonderful quote from the article:

“Data always degrade, get dirty, become obsolete or old. You need a department of 20 people like Adrian Monk [from the TV show “Monk”] who live to keep things organized.”

Brett Butler
Director of Global Sales and Marketing Practices, Lexmark International

Yes, I agree whole-heartedly – the nature of customer data (in fact, almost all database data) is that it is (1) never perfect and (2) constantly changing in ways that change.

But needing a department of twenty Adrian Monks? This is the result of Technology failing to delivery and thus requiring the Monk department to compensate.

So I’m curious, what is your organization’s Monk Factor?

The point is that with Technology pulling its weight, the Monk Factor can be very low – but it’ll never be zero. Moreover, the People part shouldn’t require that everyone have those extra-special Monk skills. After all, Adrian Monk is a genius – you can’t build a successful solution to a business problem if you require twenty Monks here and twenty Monks there… the solution won’t scale and neither will your organization. And by the way, don’t expect your Adrian Monks to complete Sisyphean Tasks either!

Use your Monks strategically and leverage the heck out of them – that approach will bring the most benefit to your organization.

The right innovative Technology can help you do just that.

It never ceases to amaze me just how much (successful) brainwashing is out there… a common misconception is that the way to deal with the pervasive nature of “imperfect data” is to somehow magically keep all of the data “perfect” all of the time.

Like Sisyphus, we’d up end never ever reaching our goal.

Instead, what we need to focus on is enabling software systems that can handle imperfect data – while still doing what we can to keep the data in reasonable shape. This is the solution to strive for – and it really is independent of the software that Netrics makes. Of course, I believe that our software delivers the best such solution but that’s beside the point.

Recently a friend of mine asked exactly how I was planning to blog about both business and technology. He pointed out the well known (but flawed) theory that says business folks are bored with technical details and technical folks are bored with business details.I love both business and technology and am particularly fond of their intersection!

I hope to offer meaningful insight on both subjects and, of course, how they need each other.